In medical imaging, x-ray pictures or sectional views of the kind obtained using modern, in particular tomographic, methods (CT, MRT, PET, etc.) are visualized to the user in the form of 2D or 3D data records on conventional or also semitransparent screens. In general, an unmodified display of such a data record is inadequate to enable the user (generally the physician) to make a reliable diagnosis or undertake planning of a therapeutic or surgical intervention on the basis of said data record. The aim is therefore generally to make anatomical objects of interest, in particular tumors, stand out clearly from the anatomical environment.
For this purpose a number of methods have been developed which enable individual measured values to be assigned to relevant anatomical structures such as nerves or fatty tissue, bone or muscle equivalent tissue but also to non-anatomical structures such as foreign bodies. These methods are known as segmentation and form the basis on which the user can observe, for example, a patient's bone structure in isolation from other tissue on the screen.
Of particular interest in medical imaging in relation to segmentation are tumors and other pathological changes within organs, as these are very poorly contrasted using conventional imaging methods, particularly in spatial display (i.e. in local space).
A tumor may be very similar to its tissue of origin in many respects. Differences are not always apparent in tomographic images. However, even if a tumor is roughly detected, it is often impossible to precisely determine the boundary with surrounding healthy tissue in the tomographic image. Even using contrast media (known as tracers in nuclear medicine), reliable and rapid segmentation is not always possible.
For this reason the user currently still has to rely to a large extent on his trained eye in conjunction with diagnostic experience in order to detect such critical structures in two- or three-dimensional CT or MRT images. In the absence of suitable programs, many physicians nowadays still segment manually: they plot the object contours into two-dimensional layer images or trace the object contour with only moderate accuracy, whereas a computer program matches the straight polyline to immediately adjacent edge pieces.
Display methods based on this in medical engineering include multiplanar reformatting (MPR), shaded surface display (SSD), maximum intensity projection (MIP) and volume rendering (VR), the MPR method more or less constituting only a 2D visualization, but of layers in all main planes or in any orientations in space (referred to in this context as orthogonal MPR and free MPR). The SSD method requires segmentation as described above, continuous surface patching being performed on the segmented subvolumes and the visible surfaces in some cases being displayed with shading.
With the MIP method, the object under examination is penetrated with imaginary so-called eye rays (from the observer's direction), the maximum intensity value in the volume being determined along each eye ray and finally displayed. This method is actually only suitable for displaying high-contrast objects such as bones or vessels filled with contrast medium.
Lastly, in the case of VR methods the entire volume is displayed with the computer taking account of local transparencies and/or shadings in such a way that the observer is conveyed a plastic representation of the structures in the volume. For this purpose, the eye ray is modulated in the entire object depth with the relevant opacity or transparency values of all the “irradiated” voxels. By means of a so-called transfer function which assigns a transparency value and an RGB value (red-green-blue value) to each gray value of the volume in question, a 3D image with depth effect is obtained on a computer screen from the totality of the eye rays. In this way, by means of the VR method, all the distinctly different objects present in the volume in question can be displayed in a 3D image in a clearly differentiable manner e.g. in different colors.
Particularly organs at a certain depth in the object under examination can be better accentuated by transparency than e.g. using the above described techniques. However, even using well conceived transfer functions and optimized illumination or shading within the framework of the latest VR methods it is not possible to display, in an easily differentiable manner, adjacent objects which differ only little in respect of their measured values. Specifically in computer tomography, “differ only little” means that these objects fall within a narrow CT value range, e.g. in a range from 1 to 10 Hounsfield units. Such difficult to differentiate objects include tumors, metastases or edemas which differ only slightly, not only in terms of brightness but also in respect of their structure relative to the surrounding healthy tissue.
The company ConVis, Medizinische Datenverarbeitung GmbH & Co. KG, Mainz, has developed a method of segmenting difficult to differentiate objects such as tumors and has presented it in the magazine “Spektrum der Wissenschaft”, Issue 1/1999 in an article by Udo Jendrysiak on pages 24 to 29. The method is essentially based on determining texture measurements such as the average gray value of a region, the average deviation of all its gray values from this mean value, the homogeneity measured by the frequency distribution of the gray values, the entropy (a measure for the disorder within a region) and the average run length (i.e. the number of consecutive voxels of the same gray value in a certain direction).
The texture measurements are determined on a surface which must have a certain minimum size. Consequently, this method requires a relatively large amount of computing time and can only indicate whether or not a region belongs to a tumor for the entire texture surface but not for an individual voxel.
The starting region for tumor segmentation consists of a polyline in a plane section which need not necessarily correspond to the original tomographic image planes. In this plane the method first determines the texture measurements of surfaces inside and outside the starting region, which, however, only provides a rough localization since, because of their size, the texture surfaces are to some extent too bulky to reach the edge of the tumor throughout. In a postprocessing phase, the environment is searched for hitherto not found tumor portions and the result in the edge area is optimized. Depending on the size of the tumor the entire process requires two to three minutes. The accuracy particularly at the edges often leaves much to be desired.